Measuring the impact of delayed access to elective cholecystectomy through patient’s cost-utility: an observational cohort study
Bibliographic record
Abstract
BACKGROUND: Deferral of surgeries due to COVID-19 has negatively affected access to elective surgery and may have deleterious consequences for patient's health. Delays in access to elective surgery are not uniform in their impact on patients with different attributes. The objective of this study is to measure the change in patient's cost utility due to delayed elective cholecystectomy. METHODS: This study is based on retrospective analysis of a longitudinal sample of participants who have had elective cholecystectomy and completed the EQ-5D(3L) measuring health status preoperatively and postoperatively. Emergent cases were excluded. Patients younger than 19 years of age, unable to communicate in English or residing in a long-term care facility were ineligible. Quality-adjusted life years attributable to cholecystectomy were calculated by comparing health state utility values between the pre- and postoperative time points. The loss in quality-adjusted life years due to delayed access was calculated under four assumed scenarios regarding the length of the delay. The mean cost per quality-adjusted life years are shown for the overall sample and by sex and age categories. RESULTS: Among the 646 eligible patients, 30.1% of participants (N = 195) completed their preoperative and postoperative EQ-5D(3L). A delay of 12 months resulted in a mean loss of 6.4%, or 0.117, of the quality-adjusted life years expected without the delay. Among patients older than 70 years of age, a 12-month delay in their surgery corresponded with a 25.1% increase in the cost per quality-adjusted life years, from $10 758 to $13 463. CONCLUSIONS: There is a need to focus on minimizing loss of quality of life for patients affected by delayed surgeries. Faced with equal delayed access to elective surgery, triage may need to prioritize older patients to maximize their health over their remaining life years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".